R Programming- Advanced Analytics In R For Data Science

R Programming: Advanced Analytics In R For Data Science, available at $109.99, has an average rating of 4.66, with 62 lectures, 3 quizzes, based on 8898 reviews, and has 61674 subscribers.

You will learn about Perform Data Preparation in R Identify missing records in dataframes Locate missing data in your dataframes Apply the Median Imputation method to replace missing records Apply the Factual Analysis method to replace missing records Understand how to use the which() function Know how to reset the dataframe index Work with the gsub() and sub() functions for replacing strings Explain why NA is a third type of logical constant Deal with date-times in R Convert date-times into POSIXct time format Create, use, append, modify, rename, access and subset Lists in R Understand when to use [] and when to use [[]] or the $ sign when working with Lists Create a timeseries plot in R Understand how the Apply family of functions works Recreate an apply statement with a for() loop Use apply() when working with matrices Use lapply() and sapply() when working with lists and vectors Add your own functions into apply statements Nest apply(), lapply() and sapply() functions within each other Use the which.max() and which.min() functions This course is ideal for individuals who are Anybody who has basic R knowledge and would like to take their skills to the next level or Anybody who has already completed the R Programming A-Z course or This course is NOT for complete beginners in R It is particularly useful for Anybody who has basic R knowledge and would like to take their skills to the next level or Anybody who has already completed the R Programming A-Z course or This course is NOT for complete beginners in R.

Enroll now: R Programming: Advanced Analytics In R For Data Science

Summary

Title: R Programming: Advanced Analytics In R For Data Science

Price: $109.99

Average Rating: 4.66

Number of Lectures: 62

Number of Quizzes: 3

Number of Published Lectures: 53

Number of Published Quizzes: 3

Number of Curriculum Items: 65

Number of Published Curriculum Objects: 56

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Perform Data Preparation in R

Identify missing records in dataframes

Locate missing data in your dataframes

Apply the Median Imputation method to replace missing records

Apply the Factual Analysis method to replace missing records

Understand how to use the which() function

Know how to reset the dataframe index

Work with the gsub() and sub() functions for replacing strings

Explain why NA is a third type of logical constant

Deal with date-times in R

Convert date-times into POSIXct time format

Create, use, append, modify, rename, access and subset Lists in R

Understand when to use [] and when to use [[]] or the $ sign when working with Lists

Create a timeseries plot in R

Understand how the Apply family of functions works

Recreate an apply statement with a for() loop

Use apply() when working with matrices

Use lapply() and sapply() when working with lists and vectors

Add your own functions into apply statements

Nest apply(), lapply() and sapply() functions within each other

Use the which.max() and which.min() functions

Who Should Attend

Anybody who has basic R knowledge and would like to take their skills to the next level

Anybody who has already completed the R Programming A-Z course

This course is NOT for complete beginners in R

Target Audiences

Anybody who has basic R knowledge and would like to take their skills to the next level

Anybody who has already completed the R Programming A-Z course

This course is NOT for complete beginners in R

Ready to take your R Programming skills to the next level?

Want to truly become proficient at Data Science and Analytics with R?

This course is for you!

Professional R Video training, unique datasets designed with years of industry experience in mind, engaging exercises that are both fun and also give you a taste for Analytics of the REAL WORLD.

In this course, you will learn:

How to prepare data for analysis in R

How to perform the median imputation method in R

How to work with date-times in R

What Lists are and how to use them

What the Apply family of functions is

How to use apply(), lapply() and sapply() instead of loops

How to nest your own functions within apply-type functions

How to nest apply(), lapply() and sapply() functions within each other

And much, much more!

The more you learn, the better you will get. After every module, you will have a robust set of skills to take with you into your Data Science career.

We prepared real-life case studies.

In the first section, you will be working with financial data, cleaning it up, and preparing for analysis. You were asked to create charts showing revenue, expenses, and profit for various industries.

In the second section, you will be helping Coal Terminal understand what machines are underutilized by preparing various data analysis tasks.

In the third section, you are heading to the meteorology bureau. They want to understand better weather patterns and requested your assistance on that.

Course Curriculum

Chapter 1: Welcome To The Course

Lecture 1: Welcome Challenge!

Lecture 2: Welcome to the Advanced R Programming Course!

Lecture 3: Learning Paths

Lecture 4: Extra: Interview with Hadley Wickham

Lecture 5: Get the materials

Chapter 2: Data Preparation

Lecture 1: Welcome to this section. This is what you will learn!

Lecture 2: Project Brief: Financial Review

Lecture 3: Import Data into R

Lecture 4: What are Factors (Refresher)

Lecture 5: The Factor Variable Trap

Lecture 6: FVT Example

Lecture 7: gsub() and sub()

Lecture 8: Dealing with Missing Data

Lecture 9: What is an NA?

Lecture 10: An Elegant Way To Locate Missing Data

Lecture 11: Data Filters: which() for Non-Missing Data

Lecture 12: Data Filters: is.na() for Missing Data

Lecture 13: Removing records with missing data

Lecture 14: Reseting the dataframe index

Lecture 15: Replacing Missing Data: Factual Analysis Method

Lecture 16: Replacing Missing Data: Median Imputation Method (Part 1)

Lecture 17: Replacing Missing Data: Median Imputation Method (Part 2)

Lecture 18: Replacing Missing Data: Median Imputation Method (Part 3)

Lecture 19: Replacing Missing Data: Deriving Values Method

Lecture 20: Visualizing results

Lecture 21: Section Recap

Chapter 3: Lists in R

Lecture 1: Welcome to this section. This is what you will learn!

Lecture 2: Project Brief: Machine Utilization

Lecture 3: Import Data Into R

Lecture 4: Handling Date-Times in R

Lecture 5: R programming: What is a List?

Lecture 6: Naming components of a list

Lecture 7: Extracting components lists: [] vs [[]] vs $

Lecture 8: Adding and deleting components

Lecture 9: Subsetting a list

Lecture 10: Creating A Timeseries Plot

Lecture 11: Section Recap

Chapter 4: Apply Family of Functions

Lecture 1: Welcome to this section. This is what you will learn!

Lecture 2: Project Brief: Weather Patterns

Lecture 3: Import Data into R

Lecture 4: R programming: What is the Apply family?

Lecture 5: Using apply()

Lecture 6: Recreating the apply function with loops (advanced topic)

Lecture 7: Using lapply()

Lecture 8: Combining lapply() with []

Lecture 9: Adding your own functions

Lecture 10: Using sapply()

Lecture 11: Nesting apply() functions

Lecture 12: which.max() and which.min() (advanced topic)

Lecture 13: Section Recap

Lecture 14: THANK YOU Video

Chapter 5: Congratulations!! Dont forget your Prize 馃檪

Lecture 1: Huge Congrats for completing the challenge!

Lecture 2: Bonus: How To UNLOCK Top Salaries (Live Training)

Instructors

Kirill Eremenko
DS & AI Instructor

SuperDataScience Team
Helping Data Scientists Succeed

Ligency Team
Helping Data Scientists Succeed

Rating Distribution

1 stars: 40 votes

2 stars: 69 votes

3 stars: 476 votes

4 stars: 2646 votes

5 stars: 5668 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

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